Briefings in Bioinformatics

Article	Citations
Cox-Sage: enhancing Cox proportional hazards model with interpretable graph neural networks for cancer prognosis	1000
Corrigendum to: Computational design of ultrashort peptide inhibitors of the receptor-binding domain of the SARS-CoV-2 S protein	467
Knowledge bases and software support for variant interpretation in precision oncology	318
Analysis of super-enhancer using machine learning and its application to medical biology	246
Computational model for ncRNA research	208
COWID: an efficient cloud-based genomics workflow for scalable identification of SARS-COV-2	205
Clustering scRNA-seq data with the cross-view collaborative information fusion strategy	196
Letter regarding article named ‘Is acupuncture effective in the treatment of COVID-19 related symptoms? Based on bioinformatics/network topology strategy’	189
DeepCheck: multitask learning aids in assessing microbial genome quality	188
GAABind: a geometry-aware attention-based network for accurate protein–ligand binding pose and binding affinity prediction	173
Balancing the transcriptome: leveraging sample similarity to improve measures of gene specificity	171
Genome sequencing data analysis for rare disease gene discovery	169
CharID: a two-step model for universal prediction of interactions between chromatin accessible regions	164
Defining the functional divergence of orthologous genes between human and mouse in the context of miRNA regulation	161
ETLD: an encoder-transformation layer-decoder architecture for protein contact and mutation effects prediction	156
CLT-seq as a universal homopolymer-sequencing concept reveals poly(A)-tail-tuned ncRNA regulation	153
Combining power of different methods to detect associations in large data sets	147
SGNNMD: signed graph neural network for predicting deregulation types of miRNA-disease associations	142
Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping	140
Clustered tree regression to learn protein energy change with mutated amino acid	133
Blood-based transcriptomic signature panel identification for cancer diagnosis: benchmarking of feature extraction methods	131
SCSMD: Single Cell Consistent Clustering based on Spectral Matrix Decomposition	123
Attribute-guided prototype network for few-shot molecular property prediction	120
Ensemble classification based feature selection: a case of identification on plant pentatricopeptide repeat proteins	118
A multichannel graph neural network based on multisimilarity modality hypergraph contrastive learning for predicting unknown types of cancer biomarkers	118

Distant metastasis identification based on optimized graph representation of gene interaction patterns	117
Computational analyses of bacterial strains from shotgun reads	115
BayesKAT: bayesian optimal kernel-based test for genetic association studies reveals joint genetic effects in complex diseases	110
mbDecoda: a debiased approach to compositional data analysis for microbiome surveys	109
A robust statistical approach for finding informative spatially associated pathways	107
CpGFuse: a holistic approach for accurate identification of methylation states of DNA CpG sites	107
AptaDiff: de novo design and optimization of aptamers based on diffusion models	103
QOT: Quantized Optimal Transport for sample-level distance matrix in single-cell omics	102
Exploring the immune evasion of SARS-CoV-2 variant harboring E484K by molecular dynamics simulations	101
A chronotherapeutics-applicable multi-target therapeutics based on AI: Example of therapeutic hypothermia	100
Correction to: Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology	97
PMiSLocMF: predicting miRNA subcellular localizations by incorporating multi-source features of miRNAs	96
Ensemble learning based on matrix completion improves microbe-disease association prediction	96
Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network	94
Directed evolution of antimicrobial peptides using multi-objective zeroth-order optimization	94
Addressing scalability and managing sparsity and dropout events in single-cell representation identification with ZIGACL	93
Building multiscale models with PhysiBoSS, an agent-based modeling tool	93
Integrating AlphaFold and deep learning for atomistic interpretation of cryo-EM maps	90
dHICA: a deep transformer-based model enables accurate histone imputation from chromatin accessibility	89
A social theory-enhanced graph representation learning framework for multitask prediction of drug–drug interactions	89
Protein phosphorylation database and prediction tools	87
scGAD: a new task and end-to-end framework for generalized cell type annotation and discovery	86
From intuition to AI: evolution of small molecule representations in drug discovery	86
Large-scale predicting protein functions through heterogeneous feature fusion	84
Identification of vital chemical information via visualization of graph neural networks	84
ADENet: a novel network-based inference method for prediction of drug adverse events	84
MicroHDF: predicting host phenotypes with metagenomic data using a deep forest-based framework	82
Assessing protein model quality based on deep graph coupled networks using protein language model	82
Machine learning modeling of RNA structures: methods, challenges and future perspectives	81
HighFold: accurately predicting structures of cyclic peptides and complexes with head-to-tail and disulfide bridge constraints	81
Multiple errors correction for position-limited DNA sequences with GC balance and no homopolymer for DNA-based data storage	80
Improving the performance of single-cell RNA-seq data mining based on relative expression orderings	80
Clover: tree structure-based efficient DNA clustering for DNA-based data storage	78
Machine learning methods, databases and tools for drug combination prediction	78
Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence	77
Subtype-DCC: decoupled contrastive clustering method for cancer subtype identification based on multi-omics data	77
Improving drug response prediction via integrating gene relationships with deep learning	77
Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution	76
ULDNA: integrating unsupervised multi-source language models with LSTM-attention network for high-accuracy protein–DNA binding site prediction	75
Learning discriminative and structural samples for rare cell types with deep generative model	74
Circular RNAs and complex diseases: from experimental results to computational models	74
Deep learning in integrating spatial transcriptomics with other modalities	73
Benchmarking of computational methods for m6A profiling with Nanopore direct RNA sequencing	73
A robust and scalable graph neural network for accurate single-cell classification	73
Detecting tipping points of complex diseases by network information entropy	72
PRIEST: predicting viral mutations with immune escape capability of SARS-CoV-2 using temporal evolutionary information	72
A review on the application of bioinformatics tools in food microbiome studies	71
Evaluating large language models for annotating proteins	71
Multi-modal domain adaptation for revealing spatial functional landscape from spatially resolved transcriptomics	70
Comparative analysis of molecular fingerprints in prediction of drug combination effects	70

IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network	70
Making PBPK models more reproducible in practice	70
Detection of transcription factors binding to methylated DNA by deep recurrent neural network	69
Mol2Context-vec: learning molecular representation from context awareness for drug discovery	68
Predicting microbe–drug associations with structure-enhanced contrastive learning and self-paced negative sampling strategy	68
scAnno: a deconvolution strategy-based automatic cell type annotation tool for single-cell RNA-sequencing data sets	68
Protein–DNA binding sites prediction based on pre-trained protein language model and contrastive learning	68
Revealing the antimicrobial potential of traditional Chinese medicine through text mining and molecular computation	66
Self-supervised learning with chemistry-aware fragmentation for effective molecular property prediction	66
ADEIP: an integrated platform of age-dependent expression and immune profiles across human tissues	66
Robust discovery of gene regulatory networks from single-cell gene expression data by Causal Inference Using Composition of Transactions	66
Correction to: sciCNV: high-throughput paired profiling of transcriptomes and DNA copy number variations at single-cell resolution	66
BatchDTA: implicit batch alignment enhances deep learning-based drug–target affinity estimation	66
Interpretable high-order knowledge graph neural network for predicting synthetic lethality in human cancers	65
A comprehensive computational benchmark for evaluating deep learning-based protein function prediction approaches	65
Inferring single-cell resolution spatial gene expression via fusing spot-based spatial transcriptomics, location, and histology using GCN	65
Inferring kinase–phosphosite regulation from phosphoproteome-enriched cancer multi-omics datasets	64
Published anti-SARS-CoV-2 in vitro hits share common mechanisms of action that synergize with antivirals	63
Comparative epigenome analysis using Infinium DNA methylation BeadChips	63
MAMnet: detecting and genotyping deletions and insertions based on long reads and a deep learning approach	63
RiboChat: a chat-style web interface for analysis and annotation of ribosome profiling data	62
A novel approach to study multi-domain motions in JAK1’s activation mechanism based on energy landscape	61
FactVAE: a factorized variational autoencoder for single-cell multi-omics data integration analysis	61
Improving multi-population genomic prediction accuracy using multi-trait GBLUP models which incorporate global or local genetic correlation information	60
HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses	59
A novel heterophilic graph diffusion convolutional network for identifying cancer driver genes	59
Concepts and methods for transcriptome-wide prediction of chemical messenger RNA modifications with machine learning	59
Complexity of enhancer networks predicts cell identity and disease genes revealed by single-cell multi-omics analysis	59
SGCLDGA: unveiling drug–gene associations through simple graph contrastive learning	59
Efficient prediction of peptide self-assembly through sequential and graphical encoding	59
Advancing microbial diagnostics: a universal phylogeny guided computational algorithm to find unique sequences for precise microorganism detection	59
scAMAC: self-supervised clustering of scRNA-seq data based on adaptive multi-scale autoencoder	58
ncRNAInter: a novel strategy based on graph neural network to discover interactions between lncRNA and miRNA	58
slORFfinder: a tool to detect open reading frames resulting from trans-splicing of spliced leader sequences	58
Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference	58
ConSIG: consistent discovery of molecular signature from OMIC data	58
A comprehensive benchmarking of differential splicing tools for RNA-seq analysis at the event level	58
Capturing large genomic contexts for accurately predicting enhancer-promoter interactions	58
ReCIDE: robust estimation of cell type proportions by integrating single-reference-based deconvolutions	57
Multi-omics regulatory network inference in the presence of missing data	57
Estimation of non-equilibrium transition rate from gene expression data	57
Deciphering the etiology and role in oncogenic transformation of the CpG island methylator phenotype: a pan-cancer analysis	57
Multilevel superposition for deciphering the conformational variability of protein ensembles	55
miRPreM and tiRPreM: Improved methodologies for the prediction of miRNAs and tRNA-induced small non-coding RNAs for model and non-model organisms	55
LRcell: detecting the source of differential expression at the sub–cell-type level from bulk RNA-seq data	55
Systematic investigation of the homology sequences around the human fusion gene breakpoints in pan-cancer – bioinformatics study for a potential link to MMEJ	55
BETA: a comprehensive benchmark for computational drug–target prediction	55
Phylogenetic inference of inter-population transmission rates for infectious diseases	54
Distinct effect of prenatal and postnatal brain expression across 20 brain disorders and anthropometric social traits: a systematic study of spatiotemporal modularity	53
The improved de Bruijn graph for multitask learning: predicting functions, subcellular localization, and interactions of noncoding RNAs	53
A novel computational model ITHCS for enhanced prognostic risk stratification in ESCC by correcting for intratumor heterogeneity	53
TransIntegrator: capture nearly full protein-coding transcript variants via integrating Illumina and PacBio transcriptomes	53
Addressing data imbalance problems in ligand-binding site prediction using a variational autoencoder and a convolutional neural network	52
Denoising adaptive deep clustering with self-attention mechanism on single-cell sequencing data	52
MiRNA–disease association prediction based on meta-paths	52
Construct a variable-length fragment library for de novo protein structure prediction	52
Detecting methylation quantitative trait loci using a methylation random field method	51
HLA3D: an integrated structure-based computational toolkit for immunotherapy	51
Therapeutic peptides identification via kernel risk sensitive loss-based k-nearest neighbor model and multi-Laplacian regularization	50
Deciphering gene contributions and etiologies of somatic mutational signatures of cancer	50
A risk assessment framework for multidrug-resistant Staphylococcus aureus using machine learning and mass spectrometry technology	49
SPANN: annotating single-cell resolution spatial transcriptome data with scRNA-seq data	49
Predicting molecular properties based on the interpretable graph neural network with multistep focus mechanism	49
A review of methods for predicting DNA N6-methyladenine sites	48
scEWE: high-order element-wise weighted ensemble clustering for heterogeneity analysis of single-cell RNA-sequencing data	48
A transformer-based deep learning survival prediction model and an explainable XGBoost anti-PD-1/PD-L1 outcome prediction model based on the cGAS-STING-centered pathways in hepatocellular carcinoma	48
Optimizing genomic control in mixed model associations with binary diseases	48
SPNE: sample-perturbed network entropy for revealing critical states of complex biological systems	48
Seq2Topt: a sequence-based deep learning predictor of enzyme optimal temperature	48
SAMURAI: shallow analysis of copy number alterations using a reproducible and integrated bioinformatics pipeline	48
RBP-TSTL is a two-stage transfer learning framework for genome-scale prediction of RNA-binding proteins	48
Development of interactive biological web applications with R/Shiny	47
Drug repositioning based on weighted local information augmented graph neural network	47
Contrastive learning-based computational histopathology predict differential expression of cancer driver genes	47
PSnoD: identifying potential snoRNA-disease associations based on bounded nuclear norm regularization	47
Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders	47
Development and validation of an explainable machine learning model for predicting multidimensional frailty in hospitalized patients with cirrhosis	46
IEPAPI: a method for immune epitope prediction by incorporating antigen presentation and immunogenicity	46
The landscape of the methodology in drug repurposing using human genomic data: a systematic review	46
PredLLPS_PSSM: a novel predictor for liquid–liquid protein separation identification based on evolutionary information and a deep neural network	46

scDeepInsight: a supervised cell-type identification method for scRNA-seq data with deep learning	46
dSCOPE: a software to detect sequences critical for liquid–liquid phase separation	46
Deep learning in structural bioinformatics: current applications and future perspectives	46
An efficient curriculum learning-based strategy for molecular graph learning	45
Matrix reconstruction with reliable neighbors for predicting potential MiRNA–disease associations	45
SAM-TB: a whole genome sequencing data analysis website for detection of Mycobacterium tuberculosis drug resistance and transmission	45
iEnhance: a multi-scale spatial projection encoding network for enhancing chromatin interaction data resolution	45
Review on predicting pairwise relationships between human microbes, drugs and diseases: from biological data to computational models	45
Benchmarking genome assembly methods on metagenomic sequencing data	44
A systematic comparison of normalization methods for eQTL analysis	44
Current computational tools for protein lysine acylation site prediction	44
Improved prediction of DNA and RNA binding proteins with deep learning models	44
Integrative analysis of multi-omics and imaging data with incorporation of biological information via structural Bayesian factor analysis	44
DeepHost: phage host prediction with convolutional neural network	44
SAM-DTA: a sequence-agnostic model for drug–target binding affinity prediction	44
Spatially contrastive variational autoencoder for deciphering tissue heterogeneity from spatially resolved transcriptomics	44
Learning single-cell chromatin accessibility profiles using meta-analytic marker genes	43
Predicting miRNA-disease associations based on graph attention networks and dual Laplacian regularized least squares	43
Differentially expressed genes prediction by multiple self-attention on epigenetics data	43
Whole-genome bisulfite sequencing data analysis learning module on Google Cloud Platform	42
Interpretable artificial intelligence model for accurate identification of medical conditions using immune repertoire	42
Microbe-bridged disease-metabolite associations identification by heterogeneous graph fusion	42
CRISP: a deep learning architecture for GC × GC–TOFMS contour ROI identification, simulation and analysis in imaging metabolomics	42
Machine learning-assisted substrate binding pocket engineering based on structural information	42
A tool for feature extraction from biological sequences	42
MUSCLE: multi-view and multi-scale attentional feature fusion for microRNA–disease associations prediction	41
scEGG: an exogenous gene-guided clustering method for single-cell transcriptomic data	41
Bioinformatics toolbox for exploring target mutation-induced drug resistance	41
scIAE: an integrative autoencoder-based ensemble classification framework for single-cell RNA-seq data	41
GSTRPCA: irregular tensor singular value decomposition for single-cell multi-omics data clustering	41
D3EGFR: a webserver for deep learning-guided drug sensitivity prediction and drug response information retrieval for EGFR mutation-driven lung cancer	41
Data-driven patient stratification of UK Biobank cohort suggests five endotypes of multimorbidity	41
toxCSM: comprehensive prediction of small molecule toxicity profiles	41
A review of biomedical datasets relating to drug discovery: a knowledge graph perspective	40
NSCGRN: a network structure control method for gene regulatory network inference	40
Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review	40
Prediction of multi-relational drug–gene interaction via Dynamic hyperGraph Contrastive Learning	40
A deep learning method for predicting metabolite–disease associations via graph neural network	40
A kinetic model for solving a combination optimization problem in ab-initio Cryo-EM 3D reconstruction	40
ComABAN: refining molecular representation with the graph attention mechanism to accelerate drug discovery	39
Learning genotype–phenotype associations from gaps in multi-species sequence alignments	39
Single-cell mosaic integration and cell state transfer with auto-scaling self-attention mechanism	39
An automatic immunofluorescence pattern classification framework for HEp-2 image based on supervised learning	39
HHOMR: a hybrid high-order moment residual model for miRNA-disease association prediction	39
MGEGFP: a multi-view graph embedding method for gene function prediction based on adaptive estimation with GCN	39
GiGs: graph-based integrated Gaussian kernel similarity for virus–drug association prediction	39
Predicting differentially methylated cytosines in TET and DNMT3 knockout mutants via a large language model	38
Predictive modelling of acute Promyelocytic leukaemia resistance to retinoic acid therapy	38
MulNet: a scalable framework for reconstructing intra- and intercellular signaling networks from bulk and single-cell RNA-seq data	38
Incremental modelling and analysis of biological systems with fuzzy hybrid Petri nets	38
Correction to: Diagnostic Prediction of portal vein thrombosis in chronic cirrhosis patients using data-driven precision medicine model	38
A comprehensive benchmark study of methods for identifying significantly perturbed subnetworks in cancer	38
Exploring the kinase-inhibitor fragment interaction space facilitates the discovery of kinase inhibitor overcoming resistance by mutations	38
Identify potential drug candidates within a high-quality compound search space	38
Optimized phenotyping of complex morphological traits: enhancing discovery of common and rare genetic variants	38
Adjustment of scRNA-seq data to improve cell-type decomposition of spatial transcriptomics	38
MAK: a machine learning framework improved genomic prediction via multi-target ensemble regressor chains and automatic selection of assistant traits	38
Combining evolution and protein language models for an interpretable cancer driver mutation prediction with D2Deep	37
Transfer learning of clinical outcomes from preclinical molecular data, principles and perspectives	37
Multi-level multi-view network based on structural contrastive learning for scRNA-seq data clustering	37
AMDBNorm: an approach based on distribution adjustment to eliminate batch effects of gene expression data	37
Evaluation of single-cell RNAseq labelling algorithms using cancer datasets	36
Identification of molecular subtypes of dementia by using blood-proteins interaction-aware graph propagational network	36
TP53_PROF: a machine learning model to predict impact of missense mutations in TP53	36
Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies	36
DRdriver: identifying drug resistance driver genes using individual-specific gene regulatory network	36
BloodNet: An attention-based deep network for accurate, efficient, and costless bloodstain time since deposition inference	36
Correction to: PHR-search: a search framework for protein remote homology detection based on the predicted protein hierarchical relationships	36
CosGeneGate selects multi-functional and credible biomarkers for single-cell analysis	35
CACIMAR: cross-species analysis of cell identities, markers, regulations, and interactions using single-cell RNA sequencing data	35
GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels	35
siRNADiscovery: a graph neural network for siRNA efficacy prediction via deep RNA sequence analysis	35
HINGRL: predicting drug–disease associations with graph representation learning on heterogeneous information networks	35
EGRET: edge aggregated graph attention networks and transfer learning improve protein–protein interaction site prediction	35
A parameter-free deep embedded clustering method for single-cell RNA-seq data	34
Correction to: Adjustment of scRNA-seq data to improve cell-type decomposition of spatial transcriptomics	34
Predicting potential small molecule–miRNA associations utilizing truncated schatten p-norm	34
Enhancing discoveries of molecular QTL studies with small sample size using summary statistic imputation	34
Molecular design in drug discovery: a comprehensive review of deep generative models	34
Letter on the results of the BASiNET method in the paper ‘A systematic evaluation of computational tools for lncRNA identification’	34
Fine-grained selective similarity integration for drug–target interaction prediction	34
Genomic privacy preservation in genome-wide association studies: taxonomy, limitations, challenges, and vision	34
Multimodal deep learning for biomedical data fusion: a review	34
Advancing single-cell RNA-seq data analysis through the fusion of multi-layer perceptron and graph neural network	34
Explainable deep neural networks for predicting sample phenotypes from single-cell transcriptomics	34
DURIAN: an integrative deconvolution and imputation method for robust signaling analysis of single-cell transcriptomics data	33
Computational models, databases and tools for antibiotic combinations	33
Integrating somatic mutation profiles with structural deep clustering network for metabolic stratification in pancreatic cancer: a comprehensive analysis of prognostic and genomic landscapes	33
MFPred: prediction of ncRNA families based on multi-feature fusion	33
Ontology-aware neural network: a general framework for pattern mining from microbiome data	33
Ensembles of knowledge graph embedding models improve predictions for drug discovery	33
Evaluation of machine learning models on protein level inference from prioritized RNA features	33
Revealing the contribution of somatic gene mutations to shaping tumor immune microenvironment	32
DKADE: a novel framework based on deep learning and knowledge graph for identifying adverse drug events and related medications	32
MiRAGE: mining relationships for advanced generative evaluation in drug repositioning	32